Search Results (363)

Soft robotics enables inherently safe, compliant interaction, yet integrating brain–computer interfaces (BCIs) remains hindered by a fundamental mismatch: BCIs typically output low-bandwidth, discrete commands, whereas soft robots possess high-dimensional, nonlinear dynamics. In this position paper, we argue that BCI–soft robot integration must move beyond direct decoder-to-actuator mapping. We propose a unified, application-oriented compatibility framework that structurally decouples hierarchical control and formally allocates authority between human neural input and local soft robotic autonomy. Crucially, we introduce verifiable, quantitative design principles that define integration as a matching problem across neural bandwidth, update frequency, latency tolerance, and control dimensionality. Through these testable hypotheses, we demonstrate that active, reactive, and passive BCIs serve distinct, complementary roles. We conclude that shared-control strategies—where the BCI provides high-level intent, target selection, or user-state feedback, while the soft robot manages low-level physical execution and interaction—offer the most practical pathway forward. We argue that future progress depends on the co-design of paradigm, decoding, control, and embodiment for neuro-adaptive and human-centred soft robotic systems. Full article

Unmanned aerial vehicles (UAVs) are widely used for monitoring and payload transport; however, their application in autonomous physical interaction remains limited due to payload constraints, stability challenges, and the complexity of integrating manipulation systems. This study presents the design and development of a lightweight foldable robotic arm based on the ten-bar Kempe Kite Inversor II linkage for UAV aerial manipulation. The mechanism generates precise straight-line motion using a single degree of freedom. Kinematic modeling and simulation validated a maximum end-effector reach of approximately 0.42 m. Structural optimization using additive manufacturing and honeycomb cellular architectures significantly reduced system weight while maintaining mechanical reliability. A passive compliant gripper, counterbalance mechanism, onboard storage net, and landing gear were integrated to evaluate the arm in a practical harvesting scenario using cherries as the test object. The final integrated system weighs 0.351 kg during operation, remaining approximately 16% below the experimentally determined UAV payload limit of 0.4185 kg. Proof-of-concept flight demonstrations confirmed successful aerial grasping of cherries, validating the feasibility of the proposed lightweight manipulation approach for agricultural applications. Full article

Soft pneumatic actuators (SPAs) are widely used in applications requiring safe and compliant interaction; however, achieving bidirectional motion within a compact and predictable architecture remains a key challenge. Existing approaches typically rely on antagonistic actuator pairs or multi-chamber designs, which increase system complexity and control requirements, while single-chamber solutions often lack robust analytical models to predict their mechanical response. In this work, a Bidirectional Origami-Inspired Soft Pneumatic Actuator (Bi-OSPA) is proposed to achieve both elongation and contraction within a single-chamber structure, where the direction of motion is governed solely by the applied pressure (vacuum or positive). The actuator leverages origami-inspired geometry, allowing deformation to be primarily described through folding kinematics, which facilitates analytical modeling. An analytical framework is developed to predict actuator deformation as well as the corresponding elastic and output forces based on geometric parameters and pressure input, and is validated experimentally, showing good agreement across the displacement range. Furthermore, the effects of key design parameters on displacement and force output are investigated and characterized. The proposed Bi-OSPA combines structural predictive capability and bidirectional functionality, providing a foundation for the design and optimization of soft actuators. Its versatility is further demonstrated through applications in achieving pure twisting when integrated with a Kresling origami unit and as an actuation unit for a one-degree-of-freedom robotic finger enabling flexion and extension. Full article

Artificial muscles are an emerging class of actuators designed to mimic the compliant, efficient, and versatile behavior of biological muscles for fields including the following: soft robotics, prosthetics, wearable enhancements, haptic interfaces, and biomedical devices. These systems encompass various actuation mechanisms, including pneumatic, hydraulic, thermal, ionic, electrochemical, and electrostatic. Each with distinct tradeoffs in voltage, strain, output force, bandwidth, efficiency, and manufacturability. Among them, electrostatic actuators have attracted increased attention due to their fast response times, high energy densities, strong compatibility with soft materials, and scalability from microscale devices to large-area and stacked actuators. However, challenges such as dielectric breakdown, material fatigue, and fabrication complexity continue to limit widespread deployment. This review presents a structured classification of various artificial muscle technologies and an in-depth examination of electrostatic actuators including dielectric elastomers, electrostrictive and ferroelectric polymers, liquid crystal elastomers, electrostatic film motors, stacked architectures, and microscale/milliscale devices. In this review the operating principles, materials, architectures, performance characteristics, and failure modes of electrostatic actuators will be discussed. Additionally, a comparison will highlight tradeoffs across actuator families based on metrics such as voltage, force, strain, bandwidth, and manufacturability. Lastly, we outline future research directions in materials, physics-informed modeling, system integration, and scalable fabrication necessary to advance electrostatic artificial muscles toward practical, real-world deployment. Full article

Conventional industrial manipulators are often costly and come with steep learning curves, which limits their scalability in hands-on robotics education. This paper presents a compact and modular vision-guided sorting platform based on a 4-DOF SCARA robot, designed for rapid assembly, reconfiguration, and beginner-friendly deployment in laboratory courses. A collaborative visual perception strategy is proposed, which introduces a lightweight YOLOv8 algorithm for robust material category recognition, while HSV-based color segmentation and Hough circle localization are utilized to extract sub-pixel centroid features. The pixel measurements are mapped to the robot base frame through an integrated nine-point hand–eye calibration model, and joint commands are generated via a joint-space quintic polynomial interpolation algorithm to ensure continuity and avoid kinematic singularities. The overall system adopts a hierarchical architecture in which the vision host communicates target commands to a motion controller via TCP/IP, while joint actuators are driven through a CAN bus. Feasibility is first verified in a Webots digital prototype with synchronized conveyor and manipulator control, and is then validated on a physical platform equipped with a compliant TPU-based soft gripper to improve grasp tolerance under localization noise. Experiments demonstrate that the system achieves an average recognition accuracy of 98.1% and a mean positioning error of 0.189 mm. The proposed platform provides an extensible testbed for teaching kinematics, perception-to-control integration, and modular robotic system development. Full article

The compliant rolling-contact element (CORE) bearing is a compliant mechanism similar to a planetary gear that provides customizable rotational torque while maintaining high radial stiffness, enabling it to simultaneously function as a parallel elastic element and a bearing replacement. This work reexamines the CORE bearing as a combined spring and bearing element for parallel elastic actuator systems. It introduces alternative CORE bearing designs, evaluates the accuracy of a previous constant-torque model proposed in the literature, describes a finite element analysis to corroborate run-up behavior, presents an optimization tool for generating bearing geometry, and includes radial stiffness experiments to assess the consequences of different fabrication methods. Together, these results provide design guidance for determining the suitability of CORE bearings for parallel elastic systems and for selecting appropriate parameters. Full article

Conventional quadruped robots are usually built with a rigid body, whereas quadrupedal mammals have flexible spines to perform agile behaviours on rough terrains. Applying a flexible spine to robots is a promising way to achieve dynamic and stable movement in extreme environments. In this paper, a novel bio-inspired spine constructed with a tensegrity structure is introduced. The prototype of the spine includes active and passive parts that can both be actively actuated and passively compliant. It has two joints with three degrees of freedom (DOF) each and can generate complex and multi-degree motions simultaneously. To control the spine with adjustable stiffness, a method based on vector closure and adjustment of pretension ratio is proposed. Several experiments are reported to illustrate the physical design of the spine and demonstrate the properties of the spine. The results demonstrate its capabilities of both active motion and passive compliance, which may improve adaptability in complex environments. Future work includes attachment of the spine to a quadruped robot to increase the overall workspace and generate rich motion skills. Full article

Soft pneumatic robotic joints driven by low-cost on/off solenoid valves are attractive for lightweight and compliant robotic systems, but precise control remains challenging because continuous actuation commands must be realized through discrete valve states subject to minimum pulse-width constraints. This paper presents a model-based constrained equivalent-control PWM (C-EC) framework for a dual-chamber bellows actuator driven by four on/off valves. An ideal duty ratio is derived so that the averaged differential pressure rate matches the desired value required to impose first-order inner-loop error dynamics. To make this law physically implementable, the ideal duty is projected onto the feasible duty set determined by the minimum reliable pulse width of the valves. The resulting duty projection error is explicitly incorporated into a Lyapunov-based analysis, yielding a uniform ultimate boundedness result for the closed-loop system under the proposed implementation and an analytical comparison with conventional discrete sliding-mode control (D-SMC). The valve flow model is parameterized through PWM step-test-based sonic conductance identification. The proposed framework is implemented on a custom 1-DOF rotary joint based on an aluminum-film spiral-duct bellows actuator. Experiments show that C-EC does not uniformly dominate D-SMC over all operating conditions, but it improves $e_{\mathrm{RMS}}$ and $R_{\Delta P}$ in the medium-to-large positive-step regime and in long-hold regulation. In the representative 45°–65°–45° step-hold test, C-EC reduced the RMS tracking error by 39.3% and the differential pressure ripple by 34.5% relative to D-SMC. In the 65° long-hold test, the RMS tracking error and pressure ripple were further reduced by 35.4% and 37.9%, respectively. A loop-period comparison also showed that a 10 ms control period reduced duty projection and pressure ripple relative to 5 ms without degrading tracking accuracy. Full article

This study presents a symmetry-guided, mechanism-informed, and constraint-aware staged evolutionary framework for the structural optimization of a heavy-duty industrial robot with dominant joint flexibility. Unlike conventional sizing strategies that treat transmission compliance as a secondary verification issue, the proposed method incorporates joint-flexibility-induced low-frequency vibration directly into the optimization formulation and organizes the design problem through a symmetric joint-space/Cartesian-space evaluation framework. An equivalent linearized flexible-joint dynamic model is established for the dominant load-bearing joints under the heavy-load operating condition of interest, and three coordinated performance indices are constructed to characterize vibration robustness, end-effector static stiffness, and global velocity-transmission quality under explicit workspace-retention constraints. To improve engineering interpretability, a staged NSGA-II strategy is adopted, in which global link-length variables and local sectional variables are optimized sequentially. The results indicate that the proposed framework increases the minimum first-order vibration frequency, reduces end-effector deformation, and preserves acceptable workspace coverage. More importantly, the optimization process reveals an interpretable asymmetry in structural sensitivity: sectional redistribution, especially in the forearm, contributes more effectively to vibration suppression than direct reduction in the global arm span. The study therefore provides a reusable symmetry-oriented structural redesign methodology for heavy-duty serial manipulators whose low-frequency dynamics are governed primarily by compliant drive chains. Full article

The digital transformation of production requires methods for integrating, storing, and operationalizing data across organizational boundaries, yet most existing approaches remain siloed and unidirectional, lacking a systematic loop from raw data to actionable knowledge and back. We introduce Data-to-Knowledge (D2K) and Knowledge-to-Data (K2D) pipelines as a universal production concept built on networks of Digital Shadows. The Data-to-Knowledge (D2K) pipeline is realized as a cross-organizational proof of concept that captures and semantically annotates robotic trajectory data from three independent research institutes and uses those data to train an inverse-dynamics foundation model for robot control. Centralized aggregation via an existing FAIR-compliant research data repository was chosen deliberately over federated alternatives to maximize semantic interoperability and reuse of shared infrastructure; federated and privacy-preserving extensions are identified as a promising future direction. Fine-tuning the cross-organizationally trained foundation model reduces training time by approximately 85% relative to end-to-end training from scratch, while achieving comparable accuracy on a standardized inverse-dynamics benchmark. These gains are attributable to the combination of cross-site data aggregation and transfer learning; isolating the contribution of semantic annotation alone remains a topic for future ablation work. The implementation demonstrates that semantically enriched, cross-organizational D2K pipelines can accelerate model development and reduce redundant data collection within a constrained but practically relevant class of robotics tasks. We further discuss limitations, governance challenges, and how these pipelines can contribute to a broader World Wide Lab for collaborative production research. Full article

Hexapod robots are attractive for operation in cluttered and uneven environments, but their walking stability is strongly affected by the coupled effects of leg morphology and foot-end trajectory planning. In many existing designs, leg-segment proportions, reachable workspace, and swing-phase trajectory smoothness are considered separately, which makes it difficult to clarify how structural parameters and motion planning jointly influence locomotion stability. To address this issue, this study presents a spider-leg-inspired hexapod robot with a simplified three-degree-of-freedom leg configuration. Selected functional characteristics of spider legs, including segmented limb structure and compliant distal contact, were abstracted into an engineering-feasible hexapod platform rather than directly reproducing spider anatomy. A parametric workspace analysis was conducted under a fixed total leg length to compare six candidate femur-to-tibia ratios. Based on forward reach, vertical foot-lifting capability, stride potential, and structural compactness, a 4:6 femur-to-tibia ratio was selected. In addition, an eleventh-order Bézier curve was developed for swing-phase foot trajectory planning and compared with a conventional composite cycloid trajectory under identical tripod-gait conditions. Simulation and straight-line walking experiments showed that the Bézier-based trajectory reduced body-attitude fluctuation and produced smoother angular-velocity variation than the composite cycloid trajectory. The results indicate that the proposed structural design and Bézier-based trajectory can improve flat-ground walking stability of the hexapod robot. This work provides a practical reference for biomimetic structural design and gait-trajectory optimization of multi-legged robots, while further validation on more complex terrain remains necessary. Full article

Humanoid robots operating near humans require short protective reaction times in physical human–robot interaction (pHRI). Safety standards distinguish between quasi-static and transient contact. This paper quantifies the reaction timing of a compliant pneumatic artificial muscle (PAM) mechanism under controlled preload conditions. Measurements were performed at 10 N, 50 N, and 100 N preload using synchronised load-cell force, PAM pressure, actuator position, and force-sensitive resistor (FSR) signals. Reaction timing was evaluated relative to the FSR-defined contact onset, at which the controller issued the pressure-release command. The force trace reached its first post-contact peak within 15–20 ms after onset, while the pressure peak occurred within 5–15 ms. A 90% recovery of the post-contact force excursion was achieved within 40–50 ms, whereas the corresponding pressure excursion required 155–180 ms. These timing results quantify reaction latency in PAM-actuated humanoid joints and support multi-modal sensing for robust onset localisation and mitigation monitoring in both ISO/TS 15066 contact types. Full article

The Compliant Ankle Rehabilitation Robot (CARR) is actuated by four Pneumatic Artificial Muscles (PAMs). These actuators mimic biological muscles, making them highly applicable in robotic systems designed to apply guiding motion to a human joint, but have complex nonlinear dynamic properties making accurate tracking control difficult. This paper presents intelligent modelling and control methods to improve the CARR’s function in a rehabilitation setting. Using the Particle Swarm Optimisation (PSO) algorithm, dynamic models of the actuators are calculated. Two model setups are proposed, a single model and dual model. Model Predictive Control (MPC) was then implemented using these models and experimentally compared with Proportional Integral Derivative (PID) and Iterative Learning Control (ILC). The results showed that PID control was less accurate than the developed control schemes, with evidence of significant chattering, as well as both over- and undershooting the setpoint. ILC performed accurately, but the required learning period and some evidence of overfitting impacted the overall performance. Single-model MPC had low error values on the X axis of the CARR and maintained the most consistent 0 displacement when an axis was intended to stay motionless. Dual-model MPC had the lowest error values on the Y axis, the smoothest motion with the least chattering, and the best performance when both axes were in motion, but showed evidence of overshooting. Based on these results, both MPC implementations have proven to be successful and suitable for future work, and the PSO modelling method is able to produce suitably accurate models for the application. Full article

This paper examines the integration of three-dimensional (3D) printing and robotic fabrication in contemporary architectural design, with a focus on overcoming the technical limitations that constrain large-scale adoption. While additive manufacturing enables the production of complex geometries and customized structures, its standalone application remains limited by fixed build volumes, planar deposition, lack of tensile reinforcement, open-loop process control, and single-process extrusion. To address these constraints, the paper proposes a functional integration framework that systematically maps robotic fabrication capabilities onto these five critical limitations. Evidence from recent studies demonstrates that such integration has already led to measurable advances, including up to a 90-fold increase in printable volume through mobile robotic systems, robotically fabricated reinforcement systems (e.g., Mesh Mold) achieving post-crack behavior comparable to conventional reinforced concrete, and the implementation of closed-loop sensor-based process control to enhance interlayer bonding. Despite these achievements, interdisciplinary collaboration across architecture, structural engineering, materials science, and robotics remains largely fragmented and is predominantly confined to academic and pilot-scale projects, such as the ETH Zurich DFAB House. Regulatory progress is also limited, with only isolated code-compliant implementations under frameworks such as ICC-ES AC509 and ISO/ASTM 52939. Persistent barriers including high capital costs, loss of information in BIM-to-fabrication workflows, anisotropic material behavior, and the absence of long-term durability standards continue to restrict widespread adoption. These findings suggest that advancing robotic additive manufacturing in architecture requires not only technological innovation but also coordinated cross-disciplinary integration, standardized testing protocols, and harmonized regulatory frameworks. Full article

Integrated die-cast components reduce machining/assembly steps and improve mechanical dynamic characteristics, eliminating joint loosening/fracture risks after long-term use. However, the highly variable geometries and random spatial distributions of burrs, flash, parting lines, and risers in castings invalidate pre-programmed or teach-in robotic grinding methods. This paper reviews recent progress and future trends in robotic grinding, analyzing four core aspects: force control stability/adaptability (e.g., adaptive impedance control can reduce average force-tracking error to 0.38 N), trajectory planning/path generation (e.g., error-driven compensation can lower contour error by 34.2–55.1%), process parameter optimization, and challenges of sensing latency/quality evaluation (e.g., deep learning models achieve 97.64% accuracy in identifying abrasive belt wear states). The key enabling technologies are summarized, including active/passive compliant force control, model-/data-driven adaptive trajectory planning, intelligent process parameter optimization integrating physical mechanisms and data-driven approaches, and multi-modal state monitoring with online quality assessment. Representative applications (metal castings, aero-engine blades, thin-walled components, weld seams) are presented, and prospective research directions are proposed. This paper provides a comprehensive reference for theoretical research and engineering practice in this field. Full article